4 Answers
4

There is a family of models that is so commonly used among practitioners that it can be almost regarded as standard. For a survey, check out Rob Almgren's entry in the Encyclopedia of Quantitative Finance. Check out also Barra, Axioma and Northfield's handbooks. In general, the impact term per unit traded currency is of the form

$$MI \propto \sigma_n \cdot \text{(participation rate)}^\beta$$

where the exponent is somewhere between 1/2 and 1, depending on the model being used, and the participation rate is the percentage of total volume of the trade, during the trading interval itself. When including the total MI in optimization, the models commonly used are the "3/2" model and the "5/3" model, in which the costs are proportional to (dollar value being traded for asset i)^{3/2, 5/3}. Since the term is not quadratic (and not solvable by a quadratic optimizer) some people approximate it by a linear term plus a quadratic one, or by a piece-wise linear convex function.

I don't believe that there is a "standard" model (per say); in fact, there are many considerations around market impact models, so you would need to be more specific. At the most basic level, you might define market as $P_{first fill} - P_{last fill}$ once your order in actually in the order book (e.g. not including other costs like "opportunity cost"). This doesn't take into account any other trades that may be taking place at the same time or other events that might be impacting the price beyond your order. It doesn't doesn't help you to forecast market impact on an impending order (which would require some knowledge of time of day, volume, volatility, etc.).

That being said, I would certainly recommend reading "Optimal Trading Strategies" (Kissell, Glantz 2003) which gives a good overview (in addition to covering other transaction cost subjects).

The easiest way ,i suppose, would be to analyze the market depth. If there is a 20 cent gap between each 100 shares on the bid then to sell 1000 shares instantly would have an impact of $2. Your average price is the midpoint. There are more complicated formulations, but this seems to be how it works on simple examples such as bitcoin exchanges.

While many models say impact is sub-linear, it souds dangerous to believe this. Given large enough size of a VWAP order I would imagine the opposite, i.e. super linear.

There is a theoretical argument as well to linear impact: Imagine 10 VWAP orders of 10 different traders making up the buy trades of a day for a security, each of them trading the same amount. If $\beta \neq 1$ then only one of them trading $10 \times$ more, would result in a different impact. Assumming they are using the same VWAP algorithm (or same broker even), this would lead to contradiction as the impact should be the same.

Given that there are many apporximations of these coefficients, each noisy and different per market, I prefer to be careful and use a linear impact model. (Of course, I have to accept that the weakness of using this pessimistic model as a PM, is that I won't have an incentive to split my large block orders to smaller ones across time... But let the execution team worry about that.)